code: def __init__(
      self, inplanes, planes, stride=1, downsample=None, norm_layer=None
  ):
      super().__init__()
      if norm_layer is None:
          norm_layer = nn.BatchNorm
      # Both self.conv1 and self.downsample layers downsample the input when stride != 1
      self.conv1 = nn.utils.spectral_norm(
          conv3x3(inplanes, planes, stride)
      )
      self.bn1 = norm_layer(planes)
      self.activation = nn.ReLU()
      self.conv2 = nn.utils.spectral_norm(conv3x3(planes, planes))
      self.bn2 = norm_layer(planes)
      self.downsample = downsample
      self.stride = stride
  
imports: import paddle
  import paddle.nn
  import paddle.nn.functional
  from paddleseg.cvlibs import manager, param_init
  from paddleseg.utils import utils
  from ppmatting.models.layers import GuidedCxtAtten
label_desc: Use image node_id 1 to create a new BasicBlock object from inherited base classes: nn with example: obj = BasicBlock(inplanes, planes, stride, downsample, norm_layer)